Physiological sensing textile apparatus

ABSTRACT

A garment system comprises a garment substrate formed from one or more textile-based sheets, a distributed array of a plurality of resistive pressure sensors coupled to the garment substrate at a set of first specified locations. Each of the plurality of resistive sensors comprises a pair of first textile-based outer layers each having an electrical resistance of no more than 100 ohms and a textile-based inner layer sandwiched between the pair of first textile-based outer layers having an electrical resistance of at least 1 mega-ohm. The system also includes electronics configured to process signals from the distributed array of resistive pressure sensors to determine one or more physiological properties of a wearer of the garment substrate.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority to U.S. ProvisionalPatent Application Ser. No. 62/827,240 entitled “PHYSIOLOGICAL SENSINGTEXTILE APPARATUS,” filed Apr. 1, 2019, the disclosure of which isincorporated herein in its entirety by reference.

STATEMENT OF GOVERNMENT SUPPORT

This invention was also made with government support under grant number1763524 awarded by the National Science Foundation. The U.S. Governmenthas certain rights in this invention.

BACKGROUND

Recently, there has been much interest in textile articles with theability to monitor physiological conditions, such as heartrate orrespiration. These so-called “smart textiles” often use flexibleelectronic components that are integrated with one or more textilelayers to form a wearable textile article.

SUMMARY

The present disclosure describes a textile article with sensorsintegrated with one or more fabric or textile sheets. The sensors canmeasure one or more of respiration, heartbeat, and posture of thewearer, and is able to do so with a loose-fitting article of clothingrather than requiring a form-fitting garment, as has typically beenrequired for existing smart textile articles that measure heart rate orrespiration.

The present disclosure describes a textile-based garment systemcomprising a garment substrate formed from one or more textile-basedsheets, one or more resistive pressure sensors coupled to the garmentsubstrate at one or more first specified locations, one or moretriboelectric sensors coupled to the garment at one or more secondspecified locations, and electronics configured to process signals fromthe one or more resistive pressure sensors and the one or moretriboelectric sensors to determine one or more physiological propertiesof a wearer of the garment substrate. In an example, each of the one ormore resistive pressure sensors comprises a pair of first textile-basedouter layers each having an electrical resistance of no more than 100ohms and a textile-based inner layer having an electrical resistance ofat least 1 mega-ohm sandwiched between the pair of first textile-basedouter. In an example, each of the one or more triboelectric sensorscomprises a pair of second textile-based outer layers each having anelectrical resistance of no more than 100 ohms and a textile-basedtriboelectric core sandwiched between the pair of second textile-basedouter layers. In an example, the textile-based triboelectric corecomprises a first textile-based dielectric layer and a secondtextile-based dielectric layer. In an example, the first textile-baseddielectric layer comprises a first textile-based dielectric materialthat forms a positively-charged triboelectric surface and the secondtextile-based dielectric layer comprises a second textile-baseddielectric material that forms a negatively-charged triboelectricsurface. The positively-charged triboelectric surface of the firsttextile-based dielectric layer is adjacent and proximate to thenegatively-charged triboelectric surface of the second textile-baseddielectric layer.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIGS. 1A-1C are several views of an example textile-based system forsensing physiological conditions of a user.

FIG. 2A is a front perspective view of an example garment with sensorsfor measuring physiological conditions of a wearer.

FIG. 2B is a rear perspective view of the example garment and sensors ofFIG. 2A.

FIG. 3 is a cross-sectional view of an example resistive sensorconfigured to measure static pressure changes for the exampletextile-based systems of FIGS. 1A-1C, 2A, and 2B.

FIG. 4 is a schematic diagram of an equivalent circuit model for theexample resistive sensor of FIG. 3.

FIG. 5 is a schematic diagram of an amplification circuit configured toprovide a measurable signal from the example resistive sensor of FIG. 3.

FIG. 6 is a cross-sectional view of an example triboelectric sensorconfigured to measure dynamic ballistic signals for the exampletextile-based systems of FIGS. 1A-1C, 2A, and 2B.

FIG. 7 is a conceptual cross-sectional view showing the operatingprinciple of the example triboelectric sensor of FIG. 6.

FIGS. 8A and 8B show a flow diagram of an example signal processingpipeline for measuring one or more physiological conditions of thewearer of the example garment of FIGS. 2A and 2B.

FIG. 9A is a graph showing the voltage response from a first resistivesensor according to FIG. 3 placed on the chest of the example garment ofFIGS. 2A and 2B and a second resistive sensor according to FIG. 3 placedon the back of the example garment while the wearer is lying in a supineposition.

FIG. 9B is a graph showing the voltage response from the first resistivesensor on the chest and the second resistive sensor on the back whilethe wearer is lying in a prone position.

FIG. 10 is a graph comparison of the raw output signal from theresistive sensor of FIG. 3, the respiration signal and theballistocardiograph (BCG) signal extracted from the raw signal using theexample signal processing pipeline of FIG. 8, and the ground truthelectrocardiograph (ECG) data during the same time period.

FIG. 11 is a graph comparing multiple traces of BCG signals from theresistive sensor of FIG. 3.

FIG. 12 is a graph comparing the triboelectric behavior of thetriboelectric sensor of FIG. 4 compared to the ground truth ECG.

FIG. 13A-13C show graphs of the steps taken to estimate the location ofBCG J-peaks for the signals from the resistive sensor of FIG. 3.

FIG. 14 is a ballistics signal power heat map as measured by the exampleresistive sensor of FIG. 3 for the front and back of the example garmentof FIGS. 2A and 2B.

FIGS. 15A-15F are pictures of several common sleeping positions.

FIG. 16 is a graph showing the voltage signal from all the resistivesensors on the example garment of FIGS. 2A and 2B as the wearer changesfrom the supine position of FIG. 15D to the fetal position of FIG. 15Eand then to the prone position of FIG. 15B.

FIG. 17 is a graph of the change in fabric resistance as a function ofthe pressure applied on one side of the example garment of FIGS. 2A and2B.

FIG. 18 is a graph showing the error for the heart rate measured by theexample garment of FIGS. 2A and 2B in various positions.

FIG. 19 is a graph showing the error for heart rate variability (HRV)measured by the example garment of FIGS. 2A and 2B in various positions.

FIG. 20 is a graph showing the error for breathing rate measured by theexample garment of FIGS. 2A and 2B in various positions.

FIG. 21 is a graph of the F₁ score for the data measured by the examplegarment of FIGS. 2A and 2B after fusion in the example signal processingpipeline of FIG. 8.

FIG. 22 is a bar graph showing the contribution to the average heartrate variability (HRV) error for different blocks of the example signalprocessing pipeline of FIG. 8.

FIG. 23 is a bar graph showing the contribution to the average heartrate error for different blocks of the example signal processingpipeline of FIG. 8.

FIG. 24 is a bar graph showing the average heart rate error for eachindividual sensor of the example garment of FIGS. 2A and 2B compared tothe error after fusion in the example signal processing pipeline of FIG.8.

FIG. 25 is a bar graph showing the average heart rate variability (HRV)error for each individual sensor of the example garment of FIGS. 2A and2B compared to the error after fusion in the example signal processingpipeline of FIG. 8.

FIG. 26 is a graph of the F₁ scores for data measured with the examplegarment of FIGS. 2A and 2B taken with the triboelectric sensor of FIG. 6activated and with the triboelectric sensor deactivated.

DETAILED DESCRIPTION

The following detailed description includes references to theaccompanying drawings, which form a part of the detailed description.The drawings show, by way of illustration, specific embodiments in whichthe invention may be practiced. These embodiments, which are alsoreferred to herein as “examples,” are described in enough detail toenable those skilled in the art to practice the invention. The exampleembodiments may be combined, other embodiments may be utilized, orstructural, and logical changes may be made without departing from thescope of the present invention. While the disclosed subject matter willbe described in conjunction with the enumerated claims, it will beunderstood that the exemplified subject matter is not intended to limitthe claims to the disclosed subject matter. The following detaileddescription is, therefore, not to be taken in a limiting sense, and thescope of the present invention is defined by the appended claims andtheir equivalents.

References in the specification to “one embodiment”, “an embodiment,”“an example embodiment,” etc., indicate that the embodiment describedcan include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

Values expressed in a range format should be interpreted in a flexiblemanner to include not only the numerical values explicitly recited asthe limits of the range, but also to include all the individualnumerical values or sub-ranges encompassed within that range as if eachnumerical value and sub-range is explicitly recited. For example, arange of “about 0.1% to about 5%” or “about 0.1% to 5%” should beinterpreted to include not just about 0.1% to about 5%, but also theindividual values (e.g., 1%, 2%, 3%, and 4%) and the sub-ranges (e.g.,0.1% to 0.5%, 1.1% to 2.2%, 3.3% to 4.4%) within the indicated range.The statement “about X to Y” has the same meaning as “about X to aboutY,”” unless indicated otherwise. Likewise, the statement “about X, Y, orabout Z” has the same meaning as “about X, about Y, or about Z,” unlessindicated otherwise.

In this document, the terms “a,” “an,” or “the” are used to include oneor more than one unless the context clearly dictates otherwise. The term“or” is used to refer to a nonexclusive “or” unless otherwise indicated.Unless indicated otherwise, the statement “at least one of” whenreferring to a listed group is used to mean one or any combination oftwo or more of the members of the group. For example, the statement “atleast one of A, B, and C” can have the same meaning as “A; B; C; A andB; A and C; B and C; or A, B, and C,” or the statement “at least one ofD, E, F, and G” can have the same meaning as “D; E; F; G; D and E; D andF; D and G; E and F; E and G: F and G; D, E, and F; D, E, and G; D, F,and G; E, F, and G; or D, E, F, and G.” A comma can be used as adelimiter or digit group separator to the left or right of a decimalmark; for example, “0.000.1”” is equivalent to “0.0001.”

In the methods described herein, the acts can be carried out in anyorder without departing from the principles of the disclosed method,except when a temporal or operational sequence is explicitly recited.Furthermore, specified acts can be carried out concurrently unlessexplicit language recites that they be carried out separately. Forexample, a recited act of doing X and a recited act of doing Y can beconducted simultaneously within a single operation, and the resultingprocess will fall within the literal scope of the process. Recitation ina claim to the effect that first a step is performed, then several othersteps are subsequently performed, shall be taken to mean that the firststep is performed before or concurrently with any of the other steps,but the other steps can be performed in any suitable sequence, unless asequence is further recited within the other steps. For example, claimelements that recite “Step A, Step B, Step C, Step D, and Step E” shallbe construed to mean step A is carried out first (or concurrently withone or more of steps B, C, D, and E), step E is carried out last (orconcurrently with one or more of steps A, B, C, and D) and, in someexamples, steps B, C, and D can be carried out in any sequence betweensteps A and E, and that the sequence still falls within the literalscope of the claimed process. A given step or sub-set of steps may alsobe repeated.

The term “about” as used herein can allow for a degree of variability ina value or range, for example, within 10%, within 5%, within 1%, within0.5%, within 0.1%, within 0.05%, within 0.01%, within 0.005%, or within0.001% of a stated value or of a stated limit of a range, and includesthe exact stated value or range.

The term “substantially” as used herein refers to a majority of, ormostly, such as at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%,98%, 99%, 99.5%, 99.9%, 99.99%, or at least about 99.999% or more, or100%.

Electronically active textiles are potentially the future of portable,interactive devices. Textiles that use all-textile sensors and/oractuators that can be woven or stitched directly into a textile orgarment are particularly exciting. While there are many smarttextile-based garments already on the market, these generally useflexible electronic components that are integrated with textiles.However, enhancing textiles with electronics is challenging because oftwo reasons: a) it can change the aesthetics and tactile perception (orfeel) of the textile, and b) the large and varied mechanical stresses towhich textiles can be subjected can abrade or damage microelectroniccomponents and electronic interconnects.

The present disclosure describes a textile garment system that uses amaterial that is likely already familiar to a user, such as cotton/silkthread and fabrics, and imperceptibly adapts it to enable sensing ofphysiological signals to yield natural fitting, comfortable, and lessobtrusive smart clothing. Specifically, the present disclosure describesa garment shirt, and more specifically a pajama shirt, as arepresentative instance of a loosely-worn and comfortable article ofclothing that many people wear at home and/or during sleep. Acomfortable, loosely-worn sleepwear garment that can measure a varietyof physiological signals continuously during sleep and other everydaysituations can be a precursor to smart clothing that looks and feelsmore like normal clothing.

While the ability to instrument everyday textiles opens up exciting newpossibilities, a challenge that has been faced is designing systems thatmeasure physiological signals when the garment is loosely worn.Currently existing technologies for sensing respiratory and cardiacsignals all rely on tightly worn bands or electrodes that are placed atspecific locations on the skin. For example, a FITBIT fitness tracker oran APPLE WATCH on the wrist is robust to a small amount of motion noisebut this is considerably less movement than what is encountered with aloose-fitting garment. Similarly, many of the ECG-sensing shirts on themarket need a tight fit at several locations on the body to obtain acardiac signal. In contrast, the garment systems and methods describedherein enable physiological sensing with a wearable garment at the otherend of the spectrum in terms of looseness, e.g., an extremely loosedaily-use textile like a pajama that is designed with comfort in mind.

While looseness may appear to present a problem, even when a garment isconsidered “loose,” there are several parts of the garment that arepressed against the body due to posture and/or contact with externalsurfaces. By carefully observing the different locations where a textilegarment is naturally pressured, several categories or classes ofpressured locations can be defined. Several of these naturally-pressuredlocations are shown in FIGS. 1A-1C, which shows the body 2 of a personin a supine lying posture (FIG. 1A), a sitting posture (FIG. 1B), and astanding posture (FIG. 1C). A first group or class ofnaturally-pressured locations are those where there is a force exertedby the body 2 on an external surface, for example, between the torso ofthe body 2 and a bed 4 (as in FIG. 1A) or between the torso of the body2 and a chair 6 (FIG. 1B). A second group or class of locations arethose where one part of the body 2 puts pressure on another part of thebody 2. For example, when the body's arm 2 rests on its side, the body 2puts pressure on the textile between the arm and the torso, e.g., at orbelow the armpit (FIG. 1C). A third group or class of locations arethose where very light pressure is exerted onto the body 2, such as fromthe weight of a blanket 8 or even pressure due to the weight of atextile (e.g., the material of a shirt being worn by the body 2) ontothe torso when the body 2 is lying down (FIG. 1A).

Often, many such pressured locations are present concurrently. Forexample, when sitting as in FIG. 1B, there is pressure between the body2 and a surface of the chair 6, such as a front surface of the chairback, between the arm and torso of the body 2, and between the chest ofthe body 2 and a textile (such as a blanket 8 or garment). When lyingdown, the same set of pressured locations exist, but there is typicallyadditional pressure between the body 2 and the bed 4 (FIG. 1A) comparedto that between the body 2 and the chair back (FIG. 1B), and there canbe additional pressure due to a blanket 8 or the garment itself pressingagainst the body 2. The inventors have even found pressure between thearm and torso and between the chest and clothing when standing and evenwhen there is no contact with an external surface.

In conjunction, these different pressured locations present a myriad ofsensing opportunities that can be leveraged to measure vital signals,such as cardiac or respiratory properties. Some devices or systems onthe market have tried to accomplish this with discrete electroniccomponents like ECG electrodes or pressure sensors. However, thesediscrete electronics typically loose the comfort and feel of the textilecompared to the textile without the electronics present. Moreover, anytime there is a noticeable change in feel (such as between a location ofthe textile without a discrete electronic device to a nearby locationwhere the discrete electronic device is present), it can be particularlynoticeable and a source of great discomfort for a person trying tosleep. Some other ECG devices on the market use textile-based ECGelectrodes, which improve on the comfort of the electrode locationsthemselves but known textile-based ECG electrodes still require tightlyworn clothing that is in direct contact with the skin to obtain a usablesignal, which can reduce overall comfort when being worn for sleep. Therequirement for a tightly-worn garment for these devices raises can alsoraise substantial robustness issues due to motion artifacts with dryelectrodes.

The inventors have found that methods and systems that sense ballisticmovements, e.g., pressure changes in the textile due to breathing andheartbeat, can overcome the limitations of existing technology describedabove and can measure these pressure changes to extract physiologicalvariables. The resulting garment systems and methods described hereinare a novel solution that leverages numerous contact opportunities tomeasure ballistic movements while being able to be incorporated incomfortable, loose-fitting textile-based sensing solutions.

There are several challenges to designing such a solution. First, thereis no existing fabric-based method to sense continuous and dynamicchanges in pressure. Existing textile pressure sensors are binarydetectors, e.g., they detect high pressure versus low pressure, but theydo not measure the amount of pressure in a continuous manner. Second,the dynamic pressure at one potential sensing location can be orders ofmagnitude different from another otherwise comparable sensing location.At one end of this spectrum, a substantial amount of body weight isplaced on a worn textile while sleeping. At the other end, there is aminuscule amount of pressure exerted by the torso onto the textileduring inhalation. Third, no single location on the body 2 has beenfound to provide a sufficiently good signal for robustly and accuratelyestimating all physiological parameters that may be of interest, and theinventors have found that in some examples, it is helpful or evennecessary to measure the signal at multiple locations and fuse theinformation together because.

The systems and methods described herein address these challenges usingseveral unique approaches. For locations where there is moderate tolarge amounts of pressure, a novel textile-based pressure sensor can beused to leverage resistive and capacitive changes to measure pressurechanges, such as those that result from respiration and heart beats. Forlocations where there is a tiny amount of pressure but where the textileis dynamic, a triboelectric textile-based sensor can be used to leveragesmall amounts of compression to extract the dynamics of the textile.These sensors can be combined in a loose-fitting textile-based garmentand their signals can be fused using a combination of signal processingand machine learning to enable holistic textile-based sensing ofphysiological variables without sacrificing comfort. The systemdescribed herein combines the novel textile-based pressure sensor andthe textile-based triboelectric sensor, and fuses signals from adistributed set of sensors to extract ballistic signals from multiplelocations.

The present disclosure describes a novel distributed multi-modaltextile-based sensor system that can be integrated with loosely-wornclothing, such as pajamas, to measure physiological signals. The systemdescribed herein can rely exclusively on textile-based elements insensed regions, while using discrete electronic components only inlocations where more rigidity is expected, such as buttons. The systemcomprising this combination of sensors was found to reliably detectphysiological signals across diverse postures and leverage multipleforms of opportunistic contact between the textile-based garment and thewearer's body.

The present disclosure also describes a method for processing thesignals from the sensors, referred to hereinafter as “the signalprocessing pipeline,” to fuse information from multiple vantage pointswhile considering signal quality from each sensor. This allows theextraction of precise information about physiological variables such asheart rate, inter-beat intervals, heart-rate variability, respirationrate, and body posture.

As used herein, the terms “body posture” or simply “posture” refers tothe position of a particular part of the body relative to anotherstructure or surface, which can include relative orientation of the bodypart of interest relative to the other structure or surface. In anexample, the garment systems and methods described herein can beconfigured to determine a posture of a person's torso, i.e., relative tothe top surface of a bed or the surface of a chair back, which might bereferred to as the “posture of the torso” or the “torso posture.” Inanother example, the garment systems and methods can be configured todetermine a posture of a person's head or neck, i.e., relative to thesurface of a pillow or a head rest, which might be referred to as the“posture of the head” or the “head posture.” In another example, thegarment systems and methods can be configured to determine the postureof one or both of a person's legs, i.e., relative to a bed or to theseat or other surface of a chair, which might be referred to as the“posture of the leg” or “leg posture.” In another example, the garmentsystems and methods can be configured to determine the posture of one orboth of a person's arms, i.e., relative to a bed or to a surface of achair, which might be referred to as the “posture of the arm” or “armposture.”

In some examples, the garment systems described herein provide forcomfortable and unobtrusive monitoring of physiological information froma wearer that can be worn continuously during long duration of wearwithout impacting sleep. To achieve these aims with loosely fittingtextiles, the sensing substrates described herein can simultaneouslycapture posture information in addition to signals that containrespiration and heart rate information. Existing sensing systems fallshort of these aims.

A variety of prior work has looked at using flexible but non-textilebased sensors that are embedded in textiles. For example, one solutionto measure vital signs uses electromechanical film (EMFi) to measureballistic heart rate. Another solution also senses ballistics usingpressure sensors printed on a polymer substrate. Several such approacheshave also been presented for posture detection using smart textiles,such as: weaving a copper wire in the back of a shirt to measure varyingimpedance due to bending of the spine; using a plastic optical fiber tomonitor spinal posture; using an array of piezoelectric sensors todetermine posture. While the sensors are flexible, they are still madeof stiff non-textile components that lack the feel of an everydaytextile. In addition, several of these require tight contact betweensensors and skin for a reliable signal, which in turn, requires tightclothing.

Other attempts have integrated discrete sensors like inertialmeasurement unit (“IMU”) sensors and pressure switches in textileelements, primarily to obtain postural parameters. In these examples,three or more IMU sensors are used to capture spinal angle, and areplaced on thoracic, thoraco-lumber, and lumber parts. However, becauseany movement would essentially be sensed by the IMU sensors, thegarments often must be tight-fitting to avoid unwanted motion of the IMUsensors that is not associated with actual movement by the wearer. Incontrast, the systems and methods described herein use no discretesensing elements and instead directly measure ballistic signals.

Some prior work has developed fabric-based sensors for physiologicalsensing. However, much of the prior work on physiological sensing withfabric-based sensors has been based on tight-fitting garments typicallyby relying on conductive fabric electrodes. While these electrodes arewidely available, they are designed for tight contact with the skin andunsuitable for loosely worn clothing. There has been some work onmeasuring impedance changes for physiological measurements, e.g., byintegrating piezoelectric elements in a smart textile to track changesin impedance using a sinusoid injected across two fabric layers. Thiswork also relies on tightly-worn clothing and close skin contact.

There has been limited work on sensing physiological variables usingloose-fitting textiles. One such work is respiration sensing usingconductive foam pressure sensors. This is essentially a binaryfoam-based sensor that moves between an open and short circuitconfiguration while a person breathes. In contrast, the systemsdescribed herein provide for complete cardiorespiratory rhythm signalwhile using far more natural fabric elements.

There are many wearable devices in the market for sleep sensing, most ofwhich use photoplethysmography to measure the pulse wave on the wrist orfingers. While these devices provide good quality heart rate andbreathing rate, heart rate variability is inaccurate and sleep postureis unavailable. Accurate heart rate variability (HRV) is difficult toobtain from wrist-worn wearables that measure pulse since the pulse wavehas a curved peak whereas modalities like electrocardiography(hereinafter “ECG”) and ballistocardiography (hereinafter “BCG”) have avery sharp and pronounced peak. In addition to accuracy issues, a keydistinction between these wearable devices and the systems describedherein is the systems described herein can be fully integrated withinexisting daily wear and does not need additional wearables.

There have also been a variety of non-contact methods that have recentlybeen tried for measuring respiration and heart rate signals. One body ofwork is on radar-based sensing of respiration and heart rhythm. Thesemethods use frequency-modulated continuous wave (“FMCW”) orultra-wideband (“UWB”) radar and measures changes in displacement andthe doppler shifts due to respiration and ballistics of the heart. Whilenon-contact sensing is appealing, presently robustness is a major issuedue to occlusion between the sensors and the subject (e.g. from ablanket, bed structure, or clothing worn by the subject), variations insleep posture, movement artifacts, and disaggregation of signals whenmultiple individuals share the same bed, just to name a few. As aresult, these methods typically are more accurate for respirationsensing which causes larger movements than ballistics of the heart.Other non-contact approaches include the use of vision-based and depthcamera-based methods such as use of cameras to find physiologicalvariables. These require line-of-sight, proper lighting and a relativelystationary user within an area in front of a camera.

Several prior approaches have explored the use of instrumentingfurniture including chairs and beds. Approaches in this body of worktypically use discrete strain gauges and custom textiles to sensechanges in pressure, such as with a textile or electronic deviceinstrumented in a chair's seat cushion to differentiate between multiplesitting postures or extracting a pressure heat map between two sheets.Several efforts have also looked at unobtrusively instrumenting beds tomeasure ballistic heart rate during sleep. One approach leverages highlysensitive geophones to measure the seismic motions induced by individualheart beats and slow-moving signals from respiration. Commercialmicro-electro-mechanical systems (“MEMS”) accelerometer-based devicesare available that can measure heart rate based on ballistocardiographysignals measured via the bed.

FIGS. 2A and 2B (collectively referred to as FIG. 2) shows an exampletextile-based system for sensing physiological conditions of a user,such as one or more of heart rate, heart rate variation, breathing rate,and posture. Some aspects of the system 10 are also shown in FIGS.1A-1C. In an example, the system 10 includes a distributed textile-basedsensor architecture that can measure cardiac and respiratory signals.The building block of the is a resistive pressure sensor that measurespressure changes. The system 10 can also include a triboelectric sensorthat measures surface charge transfer to measure tiny ballistic signals,such as small ballistic changes from the heart. In an example, thesystem includes a garment substrate 12, also referred to herein simplyas “the garment 12,” which can be worn by a person of interest for whichone or more physiological sensors is to be measured, also referred toherein as the “wearer.”

In the example shown in FIGS. 1A-1C, 2A, and 2B the garment substrate 12is in the form of a shirt that is worn on the wearer's torso, such as aT-shirt, for example a pajama shirt. As described in more detail below,the shirt form of the garment 12 allows the system 10 to determine,among other things, the posture of the wearer's torso relative to asupporting surface, such as However, the garment substrate 12 is notlimited to a shirt, but rather could be any other conceivable type ofgarment that might be worn by the wearer and can be used to form asystem that measures the posture of parts of the wearer's body otherthan the torso. For example, the garment substrate 12 could be a pair ofshorts or pants that are placed over the wearer's legs and that can beconfigured so that the system 10 can determine a posture of one or bothof the wearer's legs or hips. In another example, the garment substrate12 could be placed on or around the wearer's head or neck, such as aneye mask or a hat for the head or a ruff or turtleneck for the neck, sothat the system 10 can determine a posture of the wearer's head or neck.

In yet another example, the system 10 can include more than one garmentsubstrate 12 to determine a posture of more than one part of thewearer's body, such as a first garment substrate 12 that is a shirt anda second garment substrate 12 that is a pair of pants (e.g., a pajamaset including a pajama shirt and pajama pants). The resulting system 10can then be configured to determine a posture of the wearer's torso andthe wearer's legs and/or hips, which can provide a more completerepresentation of the wearer's total body position than a system 10 withonly a single garment substrate 12.

One or more resistive pressure sensors 14A, 14B, 14C, 14D (referred tocollectively or individually as “resistive pressure sensors 14” orsimply “resistive sensor 14”) are coupled to the garment substrate 12 atone or more first specified locations. In an example, a plurality of theresistive pressure sensors 14 form a distributed array of resistivepressure sensors that, collectively, can be configured to determine oneor more physiological parameters of the wearer with more accuracy thanmight be expected from a single resistive pressure sensor 14 or acluster of resistive pressure sensors 14 positioned in close proximity.

In an example, the system can also include one or more triboelectricsensors 16 coupled to the garment 12 at one or more second specifiedlocations. The system 10 can also include electronics (not shown inFIGS. 2A and 2B) configured to process signals from the one or moreresistive pressure sensors 14, and if present the one or moretriboelectric sensors 16, to determine one or more physiologicalproperties of a wearer of the garment 12. In an example, the electronicsare configured to process signals from a distributed array of aplurality of the resistive pressure sensors 14 to determine, among otherthings, a posture of the portion of the wearer's body on which thegarment 12 is positioned.

As used herein, the term “textile” or “textile-based,” when referring tothe substrate that forms each of the one or more layers of the resistivesensor 14 or the triboelectric sensor 16 and/or to the resultingfunctionalized textile garment 12, refers to a structure comprising oneor more fibrous structures, and in particular to threading orthread-like structures (such as yarns, threads, and the like), arrangedto collectively form a bendable, sheet-like layer of cloth or cloth-likematerial (such as by weaving or otherwise combining the one or morefibrous structures into a cloth layer). “Textiles” commonly refers tomaterials that form the cloth layers of a garment or other apparel,although the present description is not limited merely to “textiles”that are typically used for garment or apparel fabrication. That beingsaid, in some examples, the substrates that are used to form each of thesensors may be a conventional, off-the-shelf woven or non-woven fabric,such as cotton or bast-fiber fabric.

In an example, the system 10 includes several textile-based sensors 14,16, also sometimes referred to herein as “patches,” to enablemeasurement of physiological signals from multiple vantage points. Forexample, as shown in FIG. 2, the system can include a garment 12, suchas a shirt 12, for example a pajama shirt 12, with two or moretextile-based sensors attached thereto. In the example shown in FIG. 2,the system 10 includes resistive sensors 14 configured to measure thelocal pressure being applied onto the resistive sensor and one or moretriboelectric sensors 16 to measure small ballistic signals from thewearers heart. The system 10 shown in FIG. 2 includes a first resistivesensor 14A located on a front panel of the garment 12 proximate to thewearer's chest, a second resistive sensor 14B located on a rear panel ofthe garment 12 proximate to the wearer's lower back, a third resistivesensor 14C located on the left side of the garment 12 underneath theleft sleeve proximate to the wearer's left armpit, and a forth resistivesensor 14D located on the right side of the garment 12 underneath theright sleeve proximate to the wearer's right armpit, also referred to asthe front resistive sensor 14A, the rear resistive sensor 14B, the leftresistive sensor 14C, and the right resistive sensor 14D, respectively.The system 10 also includes a triboelectric sensor 16 located on thefront panel of the garment 12 proximate to the wearer's belly.

Resistive Sensor

FIG. 3 shows a cross-sectional view of an example resistive sensor 14that can be used as any one of the resistive pressure sensors 14A, 14B,14C, and 14D in the garment system 10 of FIGS. 2A and 2B. In an example,the resistive sensor 14 includes one or more highly-resistive innerlayers 22 sandwiched between two conductive outer layers 24. As usedherein, the term “highly-resistive” refers to a structure with anoverall resistance of 1 Mega-ohms (Me) or more. As used herein, the term“conductive” refers to a structure that is relatively free to conductelectrical current therethrough, for example a structure with aresistance of 100Ω or less.

In an example, the highly-resistive inner layer 22 is formed from one ormore textile-based layers. However, the design of the innertextile-based layer 22 is not straightforward as it would seem becausethe ballistic signal due to a wearer's heart rate is extremely weak. Ifthe textile substrate is an insulator like regular cotton, then theresistance is extremely high (e.g., on the order of teraohms) and it isextremely complex and expensive to design a sensing circuit to measureminute resistance changes at such high electrical impedance. Highimpedance can be desirable in the circuit to measure changes in a highimpedance sensor, but this makes the circuit very sensitive to noise,e.g., a small current induced on a high-impedance circuit results inhigher noise voltage than the same noise on a low impedance circuit.There can be many sources of noise in fabric-based circuits that uselarge conductive layers, such as electromagnetic noise, static fields,and motion artifacts. Therefore, the inventors have found it can beadvantageous to operate in a lower impedance regime to minimize theimpact of noise on the signal. On the other hand, if the textile-basedinner layer 22 is too conductive, then it can short too easily after asmall amount of pressure is applied and may not be able to cover therange of pressures that are typical in clothing, particularly clothingthat is worn during sleep. The pressure between the body 2 and anexternal surface can vary by more than an order of magnitude dependingon whether an individual is seated or lying down. Similarly, thepressure between a wearer's arm and torso is also much smaller than thepressure between the body 2 and a bed or a chair. Thus, the inventorshave found that the textile-based inner layer 22 should operate in a“sweet spot” where the fabric is optimized with sufficiently highresistance so that it does not create a short circuit even underpressure while at the same time having a resistance that is low enoughso that it will be sensitive to small pressure changes due to theballistics of the heart.

From a fabric functionalization perspective, it is also desirable toprovide for enhanced wash stability of the fabric, e.g. so that theresistive sensor 14 will be resistant to repeated wash cycles as well asdemands due to sweating, rubbing, and aging of fabric. But methods tofunctionalize the fabric to increase wash stability tend to also impactthe resistivity of the fabric, resulting in two challenges: (1) ensuringwash stability so that the impedance of the resistive sensor 14 isstable across wash cycles, and (2) finding coatings that keep theoverall resistivity of the resistive sensor 14 within desired limits.

In an example, the inner layer 22 comprises one or more functionalizedtextile layers comprising a textile substrate 26 (shown in the inset ofFIG. 3), such as a cotton fabric, onto which has been applied one ormore functionalized coating materials 28 to form one more functionalizedcoating layers 30. The functionalized coating material 28 allows theresistivity of the resulting functionalized inner layer 22 to beproportional to the pressure being applied to the resistive sensor 14.In an example, the one or more functionalized coating materials 28modify surface resistivity of the inner layer 22 compared to the textilesubstrate 26 without the functionalized coating 30. In some examples, afunctionalized coating 30 is not necessary, e.g., if the textile-basedsubstrate 26 itself has a resistivity value that is within a desiredrange.

In some examples, the one or more functionalized coating layers 30 areapplied via vapor deposition onto the textile substrate 26. In anexample, the functionalized coating material 28 comprises a hydrophobic,perfluorinated alkyl acrylate that can be vapor deposited onto thetextile substrate 26 with a vacuum reactor deposition chamber to providea perfluorinated coating 30. The perfluorinated alkyl acrylate coatingmaterial 28 imparts wash stability to the inner layer 22. Aperfluorinated coating 30 are superhydrophobic and are commonly used tocreate stain-repellant and sweat-repellant upholstery and active wear.In some examples, however, a perfluorinated alkyl acrylate surfacecoating 30 resulted in the inner layer 22 having increased resistivityas compared to a pristine, e.g., non-coated, textile substrate 26.Therefore, in another example, the chemical structure of the point wherethe coating 30 is chemical grafted onto the textile substrate 26includes a siloxane moiety, which was found to not attenuate the highsurface resistivity observed with perfluoroalkyl coatings. Withoutwishing to be bound by any particular theory, the inventors hypothesizethat such increases in surface resistivity evolved becauseperfluoroalkyl coatings contained saturated alkyl chains withoutaccessible conductive states. As most textile coatings are similarlyinsulating, the inventors believed that a surface coating that impartseither electronic or ionic conductivity to the textile substrate of theinner layer 22, such as the coating material 28 comprising the siloxanemoiety, is beneficial.

In yet another example, the functionalized coating material 28 comprisesan ion-conductive coating material 28 because ionic conductors arecomparatively more compatible with salt-rich biological systems thanelectronic materials. One example of an ion conductive coating material28 that can be used is a siloxane containing quaternary ammoniummoieties, such as N-trimethoxysilylpropyl-N,N,N,-trimethylammoniumchloride, as shown in the inset of FIG. 3. The siloxane moieties werefound to covalently bond to free hydroxyl groups present in the repeatunit of cellulose acetate (e.g., cotton) on the surface of the textilesubstrate 26, while the quaternary ammonium moieties and their chloridecounterions act as ion conductors that reduce the observed surfaceresistivity of the textile substrate 26. The surface resistivity of thecoated inner layer 22 is proportional to the surface concentration ofthe quaternary ammonium groups, which, in turn, is proportional to theconcentration of the siloxane molecule used during the solution-phasefunctionalization reaction that forms the functionalized coating 30.

Another example of an ion-conductive material that can be used as thecoating material 28 comprises a highly p-dopedpoly(3,4-ethylenedioxythiophene) (also referred to herein as “p-dopedPEDOT” or simply “PEDOT”). In an example, the p-doped PEDOT is uniformlyor substantially uniformly charge balanced with one or more counterions.In an example, the counterions comprise chloride counterions. In anexample, the concentration of chloride ions is about 10¹⁰ ions per cubiccentimeter (cm³) and a concentration variation of ±about 103 ions percm³. In another example, the counterion comprises at least one ofbromide, iodide, sulfate, acetate, formate, lactate, or combinationsthereof.

In an example, the PEDOT polymer that is used for the coating materialhas the structure of formula [A]:

where “n” is the number of repeat units. In an example, n can be 20 ormore, for example 30 or more, such as 40 or more. In an example, n isfrom about 20 to about 10,000, for example from about 50 to about 9,000,such as from about 100 to about 8,500.

Further details of one example method of applying PEDOT to a textilesubstrate is described in U.S. Patent Application Publication No.2019/0230745 A1, titled “ELECTRICALLY-HEATED FIBER, FABRIC, OR TEXTILEFOR HEATED APPAREL,” published on Jul. 25, 2019 and filed on Jan. 25,2019, the disclosure of which is incorporated herein in its entirety byreference.

In examples where the coating material 28 comprises the p-doped PEDOT,the resulting coating 30 can have an electrical resistance of from about0.1 to about 10,000 Ohms per square inch (Ω/in²). In an example, thecoating 30 formed from the PEDOT has a thickness of from about 100nanometers (nm) to about 10,000 micrometers (μm) or about 10 millimeters(mm), such as from about 100 nm to about 1 μm. in an example, thecoating 30 formed from the PEDOT coating material 29 are uniformly orsubstantially uniformly p-doped throughout the entire volume of thecoating 30, as revealed by bulk optical absorption measurements.

The weave density of the textile substrate 26 can also affect theoverall resistivity of the resistive pressure sensor 14. In an example,the textile-substrate 26 comprises a cotton gauze substrate with amedium weave density. The inventors found that a medium weave densityminimized the occurrence of shorting events in the inner layer 22 andprovided for the most stable pressure-induced electrical signals whileremaining comfortable to wear after being functionalized andincorporated into the garment 12.

In an example, each of the conductive outer layers 24 are formed fromone or more textile-based layers so that the resistive sensor 14 will bean “all-textile” sensor. In an example, each of the conductive outerlayers 24 comprises a silver nylon. The conductive outer layers 24 actas the electrodes of the resistive sensor 14, which can be connected toa detection and amplification circuit (described in more detail below).

In an example, various test sensors of the same size were created bysandwiching a sheet 22 of cotton (either pristine or functionalized withan ion-conductive coating 28) between two silver nylon fabric layers 24.As discussed above, examples where the cotton gauze substrate 22 isfunctionalized with N-trimethoxysilylpropyl-N,N,N,-trimethylammoniumchloride displayed a more sensitive voltage change with applied pressureas compared to a pristine cotton gauze or cotton lycra substrate 26.Therefore, three-layer devices containing an ion-conductive cotton gauzeproved to be efficient and simple sensor of applied pressure.

In an example, the functionalized coating 30 was shielded with anoptional protective coating 32, to impart wash stability to theresistive sensor 14. In an example, the protective coating 32 comprisesa hydrophobic material, such as a perfluorinated siloxane coating, whichcan be deposited through vapor deposition to form the protective layer32. The protective coating 32 offers an effective barrier against anydegradation of properties in the fabric resistive sensor 14 caused bythe wearer sweating, washing, rubbing and any other aging processes.

The ion-conductive coating 30 of the functionalized inner layer 22 isdifferent from previously-known commercial textile coatings, which havetypically been applied to impart hydrophobicity (e.g., forstain-repellent fabrics) or to create antimicrobial material. For bothhydrophobic and antimicrobial functionality, known coating materials areelectrically insulating and, therefore, previously-known iterations offunctionalized textiles are not usable in the design of the resistivesensor 14.

In an example, the resistive sensor 14 comprises one or two layers ofion-conductive functionalized cotton gauze as an inner layer 22,sandwiched between two sheets 24 of silver-plated nylon fabric. All thetextiles were sonicated in water for 15 min, and then rinsed withisopropanol and dried in the air prior to use. To chemically graft thesurface of the cotton gauze substrate 26, the cotton gauze substrate 26was soaked in N-trimethoxysilylpropyl-N,N,N,-trimethylammonium chloridedissolved in isopropanol (15:100 V/V), which is a precursor to thefunctionalized coating material 28 on the inner layers 22, for 30 minand then cured at 100° C. for 2 hours to form the functionalized coating30, followed by rinsing with isopropanol and drying in air. The surfaceof the functionalized cotton gauze was then modified with a vapordeposition of trichloro(1H,1H,2H,2H-perfluorooctyl) silane to form ahydrophobic protective coating 32, which provides the sensor 14 withwashability and durability. In an example, the 30-min deposition of thecoating material 28 was conducted in a custom-built, round shapedreactor (290 mm diameter, 70 mm height) under vacuum conditions, e.g.,at the constant pressure of about 1 Torr absolute. The functionalizedcotton gauze 22 was then cut into eight 10 cm by 6 cm sheets, each ofwhich was sewn around the perimeter between two corresponding 8 cm×4 cmsheets 24 of silver fabric. Sewing together each pair of these joinedgauze-silver sheets yielded four resistive sensors 14 each having thethree-layer structure shown in FIG. 3.

An electrical model of the resistive sensor can be useful in explainingand understanding its behavior under pressure. FIG. 4 shows thestructure of the three-layered resistive sensor 14 and its electricalequivalent model 34. The resistance of the functionalized inner layer 22is high enough so that it can measure a wide range of pressures but lowenough so that moderate-sized resistors can be used in the circuit tominimize noise.

The resistance through a transmission medium is inversely proportionalto the thickness of the medium, as seen in Equation [1].

$\begin{matrix}{R_{eq} = {\rho\frac{1}{A}}} & \lbrack 1\rbrack\end{matrix}$

where ρ is electrical resistivity, l is the length, and A is thecross-sectional area of the medium. In the resistive sensor 14, l isequal to the thickness of the functionalized inner layer 22, e.g.,R_(fabric) in FIG. 4.

In order to determine what aspects of applied pressure can be measured,it is helpful to see how the resistive sensor 14 works under pressure.Upon application of an inward pressure on the two outer layers 24, twosimultaneous or substantially simultaneous phenomena occur. First, thenumber of resistive routes between the two conductive layers 24increases because the air gap between the outer layers 24 is reduced. Atthe same time, the thickness of the inner layer 22 is reduced, and thecapacitance C_(EQ) of the resistive sensor 14 changes. Both of thesefactors contribute to reduction in impedance of the sensor 14 as aresult of the increase in pressure.

From a measurement perspective, it is much simpler to design a circuitto measure resistance changes than capacitance changes, therefore, in anexample, resistance changes were used to measure a ballistic signal. Tofollow the pressure applied on the sensor 14, a voltage divider was usedto produce a voltage that follows the changes in resistance of thesensor 14. This voltage contains information about the pressure appliedto the portion of the garment 12 where the sensor 14 is positioned.However, it is too coarse grained to be useful for extracting vitalsigns. In an example, the signal is filtered and amplified in the analogdomain before being used for respiration and heartbeat detection (asdescribed in more detail below).

FIG. 5 shows a schematic diagram of a circuit 36 for the resistivesensor 14 of FIG. 3. Due to the very small signal generated byheartbeats, it was desirable to increase sensitivity from the source. Inother words, the design was tuned in such a way that changes in theresistance of the resistive sensor 14 (R_(var)) can cause maximumpossible impact on the output voltage (V_(Press)). As such it isdesirable to increase ∂V_(press)/∂R_(var), as found by Equation [2].

$\begin{matrix}{V_{Press} = {\left. {V_{dd} \times \frac{R_{var}}{R_{var} + R_{1}}}\rightarrow\frac{\partial V_{Press}}{\partial R_{var}} \right. = {V_{dd} \times \frac{R_{var}}{\left( {R_{var} + R_{1}} \right)^{2}}}}} & \lbrack 2\rbrack\end{matrix}$

Equations [1] and [2] show that sensitivity decreases as R_(var)increases. Maximum sensitivity is achieved when R_(var) is much smallerthan R₁ in the circuit 36. Of course, this can be achieved by choosingan extremely large R₁. Very large output resistance of the sensor 14,however, can result in a substantial amount of noise being injected intothe electronics circuit 36. Therefore, the inventors believe that a moresensible approach is to decrease the resistance of the functionalizedinner layer 22 so that the resistance of the inner layer 22 is carefullytuned into the desired regime.

To be most effective, each resistive sensor 14 is placed at a locationwhere the garment 12, and hence the resistive sensor 14, will experiencesome appreciable amount of baseline pressure by being compressed betweentwo larger structures, such as between the wearer's body 2 and anothersurface. For example, as shown in FIGS. 1A-1C, 2A, and 2B, the garmentsystem 10 can include the front resistive sensor 14A coupled to a frontfabric layer of the garment 12 and the rear resistive sensor 14B coupledto a rear fabric layer of the garment 12. As shown in FIGS. 1A and 1B,the rear resistive sensor 14B can obtain a baseline pressure from thecompression between the wearer's body 2 and the bed 4 when she is lyingdown (FIG. 1A) or with a back of the chair 6 in which she is sitting(FIG. 1B). In both scenarios, the pressure is generated by the weight ofthe wearer's body 2 and a normal force exerted by the supporting object(e.g., the chair or the bed). The front resistive sensor 14A can obtaina baseline pressure from the weight of the blanket 8 or other coveringonto a supine wearer's body 2 (FIG. 1A) or between the wearer's body 2and the bed 4 when the wearer is lying prone (i.e., face down, notshown).

In another example, best seen in FIGS. 2A and 2B, the system 10 caninclude the left resistive sensor 14C that will be pressed between thewearer's left arm and the left side of the wearer's torso and the rightresistive sensor 14D that will be pressed between the wearer's right armand the right side of the wearer's torso. Each of the left resistivesensor 14C and the right resistive sensors 14D can be located either onthe textile that forms the side of the torso part of the garment 12 oron the textile that forms the inside part of the arm of the garment 12.For both the left resistive sensor 14C and the right resistive sensor14D, the baseline pressure is obtained from the pressure between thewearer's arm and the torso caused by the weight of the arm.

Triboelectric Sensor

As noted above, the other type of sensor that is used in the garmentsystem 10 of FIGS. 1A-1C, 2A, and 2B is a triboelectric sensor that isconfigured to measure ballistics under very low-pressure situations,such as when the fabric upon which the triboelectric sensor is attachedis resting on a wearer's torso when the wearer is standing, sitting, orlying down. In particular, the triboelectric sensor can be configuredsuch that it can detect small ballistics signals due to the wearer'sheartbeats. Even though the magnitude of positional change due to thewearer's heartbeat is quite small and is imperceptible to the naked eye,the dynamic change is quite large due to rapid changes in flow resultingin a strong ballistic force on the chest wall of the wearer.

FIG. 6 is a cross-sectional view of an example triboelectric sensor 16that can be used in the garment system 10 of FIGS. 1A-1C, 2A, and 2B. Inan example, the triboelectric sensor 16 includes a dual-layeredtriboelectric core 42 (also referred to herein as “the triboelectriccore 42” or simply “the core 42”) sandwiched between two conductiveouter layers 44. In an example, the triboelectric core 42 includes twolayers 46, 48 that act as a pair of dielectric layers made fromdifferent dielectric materials (also referred to as “the dielectriclayers 46, 48”), such that one of the layers transfers charge to theother layer due to the triboelectric effect when the triboelectricsensor 16 moves.

The conductive outer layers 44 act as the electrodes of thetriboelectric sensor. The conductive outer layers 44 can be similar oridentical to the conductive outer layers 24 of the resistive sensor 14shown in FIG. 3, such as by being made from a silver nylon fabric. Theconductive outer layers 44 can be connected to a detection andamplification circuit (described in more detail below).

When the two dielectric layers 46, 48 of the core 42 come into contact,static charging occurs over the contacting surface area. Overall, thecharge on the triboelectric sensor 16 remains at zero due to the chargesbeing located in or substantially in the same plane. Upon subsequentseparation of the dielectric layers 46, 48, which results in aseparation of the charges, an alternating current between the conductiveouter layers 44 is induced to compensate for the charge imbalance, withthe generated charges being collected on the conductive outer layers 44,as shown conceptually in FIG. 7. The open-circuit voltage V_(OC) isdependent on the surface charge density (σ), the separation distancebetween the dielectric layers (x(t)), and the permittivity of free space(ϵ₀), as shown in Equation [3].

$\begin{matrix}{V_{OC} = \frac{\sigma\mspace{14mu}{x(t)}}{ɛ_{0}}} & \lbrack 3\rbrack\end{matrix}$

The operation of the triboelectric sensor 16 can be maximized when thecharge transfer between the dual layers 46, 48 of the core 42 isoptimized, preferably such that the charge transfer is as high as ispractical. When optimizing the triboelectric sensor 16 for sensing, theparameter that can be modified is the surface charge density (σ), sincethis defines the overall sensitivity of the magnitude of generatedvoltage to joint motion (x(t)). Specifically, the voltage generated bythe triboelectric sensor 16 is related to the speed of contact andseparation between the two layers 46, 48 of the core 42, which allowsfor the detection of ballistic changes due to heartbeats.

As is known from an understanding of the triboelectric effect, thesurface charge density σ can depend on the materials used to form thetwo dielectric layers 46, 48 of the triboelectric core 42. In anexample, the first layer 46 of the triboelectric core 42 comprises apositively-charged triboelectric surface 50, also referred to as “thepositive triboelectric surface 50” or “the positively-charged surface50”). Because of its charge, the first layer 46 may also be referred toherein as “the positively-charged triboelectric layer 46” or “thepositive triboelectric layer 46.” In this example, the second layer 48of the triboelectric core 42 comprises a negatively-chargedtriboelectric surface 52, also referred to as “the negativetriboelectric surface 52” or “the negatively-charged surface 52”). Thenegative triboelectric surface 52 is adjacent and proximate to (or incontact with) the positively triboelectric surface 50 of the positivetriboelectric layer 46. Because of its charge, the second layer 48 mayalso be referred to herein as “the negatively-charged triboelectriclayer 48” or “the negative triboelectric layer 48.”

In an example, the positive triboelectric layer 46 comprises a cotton orcotton lycra based substrate, which can be further functionalized toimprove charge transfer. In an example, the cotton or cotton lycra basedsubstrate is functionalized with one or more silane moieties, such as asilane moiety comprising an amine group to act as the positively-chargedtriboelectric surface 50, for example an aminopropyl siloxane, as shownin the inset of FIG. 6. In an example, the negative triboelectric layer48 comprises a fabric substrate 54 that is coated with a negativetriboelectric material 56, such as polyurethane. In an example, thenegative triboelectric material 56, e.g., the polyurethane, is coatedonto a ripstop nylon substrate 54. The inventors found that thepolyurethane coating 56 on the substrate 54, such as the polyurethanecoating 56 on the ripstop nylon substrate 56, displays a negativesurface charge value, on average, under the conditions in which thetriboelectric sensor 16 will typically experience because of thepresence of the negative triboelectric material 56, i.e., polyurethane.

When the positively-charged triboelectric surface 50 comes into contactwith the negatively-charged triboelectric surface 52 and the dual layers46, 48 of the triboelectric core 42 are sandwiched between theconductive outer layers 44, it forms a triboelectric device that can actas the triboelectric sensor 16. When pressure is applied to thistriboelectric sensor 16, the two oppositely-charged layers 46, 48 areforced into physical contact, upon which a small amount of surfacecharge transfer occurs, creating an observable electrical signal.However, this charge transfer event is quickly reversed such that thesignal quickly decays, even if constant pressure is applied to thetriboelectric sensor 16. Due to this behavior, the triboelectric sensor16 is well suited for detecting dynamic changes in pressure, such asthose that can occur as a result of the ballistics of the heart.

In an example, the triboelectric sensor 16 comprises apolyurethane-coated ripstop nylon as the negative triboelectric layer48. To provide a cotton lycra with a positively-charged triboelectricsurface 50, the cotton lycra fabric was soaked in (3-aminopropyl)trimethoxysilane in a hexane solvent (10:100 V/V) for 30 min, followedby rinsing with isopropanol and drying in air, to provide afunctionalized positive triboelectric layer 46. The positivetriboelectric layer 46 and the negative triboelectric layer 48 were thencut into 17 cm by 13 cm sheets and sewn together as they were beingplaced between two 15 cm×11 cm sheets of silver nylon fabric 44.

Two Complimentary Sensor Types

As described above, both the resistive sensor 14 and the triboelectricsensor 16 can detect a cardiac ballistic signal. However, in somepreferred examples, the garment system 10 includes both a resistivesensor 14 and a triboelectric sensor 16. The reason for using bothsensors 14, 16 rather than just the resistive sensor 14 or just thetriboelectric sensor 16 is because the resistive sensor 14 can operateunder pressure, i.e. it can measure ballistics when a sufficientbaseline pressure has been exerted on it. In contrast, the triboelectricsensor 16 is able to operate under very light pressure, for example, dueto the weight of the textile material of the garment resting on thewearer's body 2 or the weight of a blanket 8 resting on top of thegarment system 10 and the wearer. Under higher pressure, there isinsufficient change in distance between the core layers 46, 48 of thetriboelectric sensor 16 to cause measurable change in charge transfer.Thus, the two types of sensors are complementary and cover medium tohigh pressure situations (with the resistive sensor 14) and low-pressuresituations (with the triboelectric sensor 16).

Assembling the Garment System

Having designed the individual textile-based sensors 14 and 16, asdescribed above, the sensors 14, 16 were interconnected with one or moreconductors 58 in a way that reduces and, in some examples, minimizes thenumber of discrete hard electronic components. In an example, thegarment system 10 is designed for maximum comfort, particularly when thegarment 12 is designed for measurement of physiological conditions whilethe wearer is sleeping. Therefore, in some examples, the use of metalwires as one of the conductors 58 was avoided and, in some examples, thegarment system 10 is completely devoid of wires. Instead, in an example,the one or more conductors 58 of the garment system 10 are conductivethreads shielded by normal cotton to act as signal conductors throughthe garment 12. In one example, silver-plated nylon threads 58 were usedas the conductors 58. The threads 58 were shielded in a fabric rod madefrom cotton and attached to the conductive outer layers 24 of theresistive sensors 14 and the conductive outer layers 44 of thetriboelectric sensor 16 via snap buttons.

Using these conductors 58 (e.g., the conductive thread), the sensors 14,16 can be connected to two circuit boards depending on whether thesensor being connected is one of the resistive sensors 14 or thetriboelectric sensor 16. While these can potentially be combined into asingle platform, two separate circuit boards were used in an example forease of prototyping. The circuit boards were designed with a small formfactor, roughly the size of quarter or a large button. The inventorsbelieve that the size can be further shrunk down to half the currentsize or smaller after further engineering and due to the capabilities ofmass production version individual prototype fabrication. It is believedthat in some examples, the sensors 14, 16 can be integrated into thebuttons or other common hard structures of the garment 12.

In an example, the PCB board for the resistive sensors 14 is a singlecustom-designed PCB board that performs the filtering, amplification,and communication (described below). BCG signals are typically withinthe 1-10 Hz frequency range, and the peak power of the BCG signal istypically in the 7-8 Hz frequency bin. This information was leveraged tochoose a cutoff frequency of 4-10 Hz for faster DC rejection andcapturing the strongest BCG frequency component.

Another challenge for the garment system 10 is the removal orminimization of noise in indoor environments. As can be seen in FIG. 10,an example raw signal obtained from the sensors 14, 16 contains strongpower line noise. The inventors believe that the substantial power linenoise is due to several reasons, including the very large sensor outputimpedance, large sensor surface, as well as close proximity to thewearer's body 2. While typical ECG processing boards filter power linenoise using a differential amplifier, the asymmetry in exposure of theconductive layers 24 (one exposed outside and the other inside) made itdifficult or impossible to fully suppress noise using this method.Therefore, in an example, a 5th-order low pass filter was designed toreject power line noise. In an example, the gain of the board is around50 dB. Each pressure sensing sensor 14 is connected to one analogpipeline drawing around 150 μA of current.

A second PCB board was designed for triboelectric signal amplification.In an example, this amplification board comprises a differentialamplifier followed by multiple filtering and amplification stages tocapture small movements of the ballistic signal. In an example, thecutoff frequency of the triboelectric sensor board is 4-10 Hz. Since thetriboelectric sensor 16 experiences very minute movements of the skin,the gain is about 80 dB, which is larger than the resistive sensorboard. The overall current consumption of the triboelectric sensor boardis around 2 mA.

Optimizing Sensor Placement

The location of the sensors can be an important factor in theperformance of the garment system 10 because the signal detected by eachtype of sensor 14, 16 is sensitive to placement. While this process mayeventually be optimized to different body types or even personalized, inone example sensor placement was optimized with respect to oneindividual wearer (also referred to as the calibrating wearer) and usedthe same settings across various other wearers.

To find the best placement for the resistive sensors 14A and 14B on thefront and rear of the garment 12, resistive sensors 14 were placed atdifferent locations and the signal quality was measured while thecalibrating wearer was lying down face down (prone) and face up(supine), respectively. The measurement setup was carefully done tominimize folds in the textile of the garment 12 and random bodymovements so that the effect of the position of the sensors 14, 16 onthe BCG output signal could be isolated.

The rear resistive sensor 14B was placed on twelve (12) differentlocations on the rear of the garment 12 and for each location, five (5)measurements were taken, each with a duration of 30 seconds, resultingin a total of 150 seconds of data for each location. Then, J-peaks weremanually labeled and the average amplitude across all J-peaks wasconsidered as a signal quality factor for each sensor 14, resulting in a3×4 matrix. The result was then interpolated to achieve higherresolution. A similar procedure was performed for the front resistivesensor 14A on the front of the garment 12 (i.e., twelve locations, fivemeasurements of 30 seconds each for each location). A heat map wasgenerated from the resulting amplitudes for the locations of the frontresistive sensor 14A and the rear resistive sensor 14B, which are shownschematically in FIG. 14.

It was observed that the front resistive sensor 14A had superior signalstrength compared to the rear resistive sensor 14, especially in thestomach area. Although not wishing to be bound by any theory, theinventors believe that this is because the wearer's spine and rib cagediminish power of heart ballistics. The placement of the triboelectricsensor 16 was also empirically determined. Only one triboelectric sensor16 was used to reduce the complexity of dealing with too many sensors.While multiple locations may have worked for the triboelectric sensor16, it was noticed that the worst posture for the resistive sensor 14was when the wearer was lying on his or her back, particularly when thewearer has high body weight. In this case, the triboelectric sensor 16could compensate for a poor signal from the resistive sensor 14 since itcan provide an accurate heart rate signal even when only a textile(i.e., of the garment 12 and/or a blanket 8) is lying on the wearer'schest. Since a resistive sensor 14 and a triboelectric sensor 16 couldnot be placed at the same location, in an example, the front resistivesensor 14A was moved to the second-best position, which was the wearer'schest rather than proximate to the wearer's stomach.

Signal Processing of Sensor Outputs

FIGS. 8A and 8B (collectively referred to as “FIG. 8”) is a schematicflow diagram of an example algorithm 60 for analyzing output signals 62from the garment system 10, e.g., signals corresponding to pressuremeasurements from each of the one or more resistive sensors 14 andballistic signals from the triboelectric sensor 16. It may help to thinkof the signal processing algorithm 60 shown in FIG. 8 as a “pipeline”through which the signals pass and are “processed” to provide a final“product,” i.e., one or more usable output values 64A, 64B, 64C(collectively “output values 64” or “output value 64”) that predictablycorrespond to one or more physiological conditions of the garmentwearer. For this reason, the algorithm 60 of FIG. 8 may also be referredto as “the signal processing pipeline 60” or simply “the pipeline 60.”

In an example, the goal of the signal processing pipeline 60 is toprovide a comprehensive set of physiological measures of one or more ofrespiratory and cardiac rhythm including one or more of breathing rate64A (labeled as “BR 64A” in FIG. 8), heart rate (“HR 64B” in FIG. 8),and heart rate variability (“HRV 64C” in FIG. 8). These physiologicalparameters are useful for many applications including, but not limitedto, sleep stage classification, sleep quality estimation, recoveryduring endurance training, stress management, and disease prediction. Insome examples, the combination of sensors 14, 16 and the signalprocessing pipeline 60 can determine sleep position in addition tocardiac and respiratory rhythm by leveraging the presence of the severalsensors 14, 16 in the garment system 10.

The central challenge of processing the output signals 62 from the oneor more resistive sensors 14 and the triboelectric sensor 16 is that thesignals observed by the sensors 14, 16 depend on several factorsincluding the wearer's posture, the wearer's weight, the fit of thegarment 12, and the extent of contact between the garment 12 and thewearer's body 2. An example of this difficulty is shown in FIGS. 9A and9B, which show the signals from the front resistive sensor 14A (dataseries 200) and from the rear resistive sensor 14B (data series 202) andcompares both to the ECG ground truth signal (data series 204). FIG. 9Ashows the data when the wearer is laying in a prone (i.e., face down)position, while FIG. 9B shows the data from the sensors when the weareris lying in a supine (i.e., face up) position. As can be seen from FIGS.9A and 9B, for both the prone position and the supine position, one ofthe sensors performs poorly while the other sensor provides a clearersignal. Therefore, it is believed that to best obtain robustphysiological measurement under different real-world situations, it willuseful to fuse information from different sensors.

Pre-Processing Stage

Returning to FIG. 8, a first stage of the example signal processingpipeline is a signal pre-processing stage 66, also referred to simply as“pre-processing 66.” The pre-processing stage 66 acts to filter outvarious noise sources (to the extent possible). The output voltage 68 isa combination of DC offset generated by amplifiers (e.g., the exampleamplifier circuit of FIG. 5), low-frequency components corresponding torespiration, higher frequency components corresponding to the BCGsignal, and noise in all frequency bins. At the pre-processing stage 66,the DC baseline 70 from each resistive sensor 14 can be directly used toobtain two measures: a) posture based on relative pressure across thesensors; and b) respiration based on baseline pressure variations.

The DC baseline 70 directly provides the pressure for each resistivesensor 14 which, in turn, provides information about the contact betweenthe various sensors 14 and the wearer's body 2. This information can befused to determine posture at 72. When different DC baseline signals 70were measured for different postures, it was found that the baselinesignals 70 from the resistive sensors 14 are highly distinct. A simpledecision tree 74 can very accurately distinguish between differentpostures. For example, if the front resistive sensor 14A has a high DCbaseline 70 and the rear resistive sensor 14B has a low DC baseline,then it can be assumed that the wearer is laying in face-down posture.If the left resistive sensor 14C has a higher DC baseline 70 than theright resistive sensor 14D, than it can be determined that the wearer isleaning toward his or her left side (with the amount of lean beingproportional to the amount of the DC baseline for the left resistivesensor 14C as compared to the right resistive sensor 14D).

The DC baseline can also be used to obtain the respiration rate (alsosometimes referred to as simply “RR”) of the wearer in a straightforwardmanner. It was determined that the respiration rate of the wearer can beaccurately estimated with a two-step sub-process. First, the frequencybin with the highest power is found and is determined to correspond tothe respiration signal. Second, band-pass filtering based around the FFTpeak is performed to avoid counting fluctuations of the second harmonic.The result of the band-pass filter is a signal oscillating around zero.The number of zero crossings are counted and divided by the duration ofthe signal to find the duration of a half cycle. Since a respirationmeasure can be obtained from each resistive sensor 14, the median acrossall of the resistive sensors 14 (e.g., the front, rear, left, and rightresistive sensors 14A, 14B, 14C, and 14D in the example garment system10 of FIGS. 1A-1C, 2A, and 2B) is determined to obtain an aggregatemeasure of the wearer's respiration rate.

Determining an accurate measure of heart rate (also referred to hereinas “HR”) is more challenging, particularly if the goal is to getaccurate detection of BCG peaks in order to estimate heart ratevariability (also referred to herein as “HRV”). A voltage sample at eachresistive sensor 14 in the pre-processing stages 66 is shown in FIG. 10.As can be seen, the respiration signal 80 is quite clear, but the BCGsignal 82 is more variable and has many peaks that could bemisclassified as heart beats. The rest of the signal processing pipeline60 is designed to detect individual heartbeats and peak locations.

Heart Rate Processing Via Feature Extraction from Sensors

A BCG signal 82 can be dependent on which type of sensor 14, 16 is beingused to determine the signal 82. As described above, each resistivesensor 14 measures pressure changes whereas the triboelectric sensor 16measures surface charge transfer. Since these are very different typesof signals, different feature extraction techniques are used for eachsensor 14, 16.

Resistive Sensor

ECG feature extraction has been studied for many decades, applyingexisting techniques to the extraction of BCG features from the resistivesensor is non-trivial for at least two reasons. First, the BCG signal 80varies depending on where the sensor 14 touches the wearer's body 2. Thereason for this being that the ballistic signal detected by theresistive sensor 14 is impacted by the skeletal structure, particularlythe spine. Second, the types of noise in the resistive sensor 14 alsodiffer because motion-induced artifacts like static noise are differentacross the different locations of the resistive sensors 14. Thisdiversity means that traditional detectors can provide sub-optimalperformance when subject to these variations. The signal processingpipeline 60, therefore, uses unsupervised methods for robust featureextraction to deal with a range of signal variation and noise sourcesobserved in the ballistic signal.

In an example, the signal processing pipeline 60 uses sparse coding,which can leverage vast amounts of unlabeled data to generate features.Sparse-coding methods have also been applied to a limited extent in thecontext of ECG signals and BCG signals. The general concept of sparsecoding for physiological waveforms is to extract a dictionary offeatures 86 for detecting the various peaks (e.g. the P, Q, R, S, and Tpeaks in the case of an ECG) in a robust manner despite extremely noisydata. In the context of the garment system 10, sparse coding was used tolearn a sparse dictionary of shapes of the ballistic signals observed atdifferent locations of the resistive sensors 14 on the garment 12.

Sparse coding is a method for representing a feature vector X in termsof sparse linear combinations Σ_(k=1) ^(K) α_(k)B_(k) of a set of Kbasis vectors, B_(k). Given a set of basis vectors B_(k), the sparsecoefficient vector α_(k) is computed as the solution to the l₁regularized optimization problem of Equation [4].

$\begin{matrix}{{\underset{\alpha}{\arg\mspace{14mu}\min}\mspace{14mu}{{X_{n} - {\sum\limits_{k = 1}^{K}\;{\alpha_{k}B_{k}}}}}_{2}^{2}} + {\lambda{\alpha }_{1}}} & \lbrack 4\rbrack\end{matrix}$

Given a data set D={X_(n)}_(n=1:N), the basis is learned to minimizeerrors between each data case and its reconstruction with the constraintof sparse coefficients. The typical approach to solve this is by usingan alternate minimization strategy. The goal of the sparse coding of thesignal processing pipeline 60 is to determine the highest BCG peak, alsoknown as J-peak, using sparse coding. A peak detector with a fairlyrelaxed threshold is applied over the signal to over-generate candidatepeaks. FIG. 11 shows several instances of such a window overlaid on topof each other for the rear resistive sensor 14B. The BCG waveformobserved in FIG. 8 for the garment system 10 is very similar to thepattern presented in literature.

Note that sparse coding can be used to learn an over-complete basis in afully unsupervised manner. This is attractive because it means that anew user need not provide labeled data. Rather, the dictionary 86 cansimply be expanded by leveraging raw data from a new user. This canprovide for the construction of a more representative population-leveldictionary 86 without requiring additional labeling overhead for a newuser.

Using parameters defined for sparse coding, a dictionary 86 of basisvectors is learned from the time series windows that have been croppedover candidate peaks in a pre-processing and window extraction step 88.As a result, each window can be represented by a series of weightscorresponding coefficients for linear combination of dictionary elementsto recreate the window. These weights are used as features for theclassification stage 90 of the signal processing pipeline 60 of FIG. 8.

Triboelectric Sensor

The signal obtained from the triboelectric sensor 16 is different fromthe canonical BCG shape that is observed with movement (or otherpressure) sensors. In the case of the triboelectric sensor 16, what isbeing measured is the charge and discharge of the triboelectricmaterials which approximately corresponds to how it compresses andreleases as a consequence of ballistics caused by the wearer's heartbeat(as described above).

FIG. 12 shows an example of the triboelectric waveform signal 92. As canbe seen, the ballistics of the heart causes the triboelectric signal 92to oscillate much like a spring-mass system with some damping due to thetextile properties of the garment 12. FIG. 12 also plots the envelope 94of the triboelectric signal 92 (also referred to herein as “thetriboelectric envelope 94”), with the assumption that the amplitude ofthe envelope 94 roughly correlates with the amount of mechanical energyon the wearer's skin surface.

In an example, a J-peak based method to extract features from thetriboelectric signal 92 was not used because the triboelectric sensor 16is observing a derived signal that is induced by the ballistics of theheart. Also, the signal peak is variable and unstable since there isrelatively weak contact between the triboelectric sensor 16 and thewearer's body 2, at least in the example of the garment system 10 shownin FIGS. 1A-1C, 2A, and 2B where the triboelectric sensor 16 is locatedon the wearer's stomach where the triboelectric signal 92 is impacted byposture and how the textile 12 rests against the wearer's body 2.

For this reason, in some examples, instead of using an analysis of thepeaks of the triboelectric signal 92, the signal processing pipeline 60can use the triboelectric envelope 94 as the source of features. Thetriboelectric envelope 94 loses information about the location of thepeaks but is more robust to outliers. After determining thetriboelectric envelope 94, the inventors have found that typically thereis a correlation between the locations of peaks of the triboelectricenvelope 94 and the expected location of a J-peak. Using this insight,in an example, multiple samples can be taken from the triboelectricsensor 16 over a regular interval and those values can be used astriboelectric features for classification. In one example, five (5)samples of the triboelectric envelope 94 were taken with a100-millisecond interval and were used to determine the triboelectricfeatures.

J-Peak Classification

The next stage of the signal processing pipeline 60 classifies thecandidate peaks into valid or invalid BCG J-peaks. This stage isexecuted on a per-sensor basis, i.e., the peaks for each sensor areclassified separately in this stage and are then fused in subsequentstages.

To perform J-peak classification, the first step is to collect labeleddata using an ECG sensor as ground truth. Depending on placement of eachtextile-based sensor, the BCG J-peak will have a small delay relative toits corresponding ECG R-peak. This delay is called the RJ duration andis affected by many factors including an individual's medical conditionand placement of the sensor. This duration can reach up to 300milliseconds. To account for this delay, the largest peak that appearswithin a 400 ms window after an ECG R-peak is labeled as the BCG J-peak.Then, a few cases per sensor were manually checked to validate thelabeling of the BCG J-peak.

In an example, the signal processing pipeline 60 uses five (5) sets offeatures for the classifiers: (1) the sparse coding feature weightscorresponding to the dictionary (determined as described above), (2)posture information coming from the DC baseline 70, (3) amplitude of thepeak, (4) the multiple samples from the triboelectric envelope 94centered around the peak, and (5) multiple samples from the envelope ofthe resistive sensor 14 centered around the peak. In an example, thesefeatures are used to classify each candidate peak.

Once the features are determined, the classification model 90 can be anysimple machine learning model. In an example, a linear support-vectormachine (“SVM”) was used as the classification model 90. However, thosehaving ordinary skill in the art will appreciate that other models maybe equally viable. The classifier model 90 is trained based on sparsecoding weights and other time-domain features described herein and thelabels provided for each candidate peak. At this stage, a classificationscore can also be determined for the classification for each peak. In anexample, the classification score is the signed distance from the SVMdecision boundary. In an example, the classification score can be usedin the fusion stage (described below) to combine the data from multiplesensor streams and improve the overall results.

Multi-Sensor Fusion Stage

Next, the signal processing pipeline 60 enters a fusion stage 96, whichfuses the outputs of the individual per-sensor classifiers to determinethe location of each J-peak in a more accurate manner. The fusion stage96 is part of an overall post-processing phase 100 of the signalprocessing pipeline 60.

In an example, the fusion stage 96 includes determining an estimate ofthe quality of the measurements from each sensor 14, 16. In an example,this is done by first defining a signal quality index 98 that seeks toidentify which sensors provide the most relevant information so thatmore weight can be assigned to the output from these sensors. In anexample, the signal quality index 98 is based on the observation that apoor-quality sensor generally has high variance in the inter-peakintervals since it has more false positives and false negatives. In anexample, the signal quality index 98 (SQI) is defined by Equation [5].

$\begin{matrix}{{SQI}_{p,u,s} = {1\text{/}{{std}\left( {II}_{p,u,s} \right)}}} & \lbrack 5\rbrack\end{matrix}$

where II_(p,u,s) refers to an array of inter-beat intervals for eachmeasurement on user u, in position p, and from sensors. Each element ofthis array is calculated as the duration between two correspondingconsecutive peaks classified as correct J-peaks, as shown in Equation[6].

$\begin{matrix}{{{II}_{p,u,s}(i)} = {{T_{p,u,s}^{j}(i)} - {T_{p,u,s}^{j}\left( {i - 1} \right)}}} & \lbrack 6\rbrack\end{matrix}$

Given the signal quality index 98 per sensor and classification scorefor each peak from the SVM classifier, a score for each peak, i, isdefined as the weighted sum across all sensors. In other words, thescores are summed up the across the different sensors that detect thesame peak (within a short time window of each to adjust for timingdifferences), as in Equation [7].

$\begin{matrix}{{{Fused}\mspace{14mu}{{Score}(i)}} = {\sum\limits_{x = {1{\ldots 4}}}{{{Score}(s)}*{{SQI}(s)}}}} & \lbrack 7\rbrack\end{matrix}$

Next, the peaks that have positive scores after the sensor fusion 96 areselected as the detected J-peaks. There may still be some stragglersthat have been missed by this assumption, therefore, another sweep ofthe resulting inter-beat intervals is performed. For cases where heartrate variance (HRV) exceeds possible range for humans, another peak withthe second highest fused score is selected and is added to thedetection. At this point, estimated locations of the J-peaks are used inorder to calculate the physiological parameters, specifically heart rate(HR) and heart rate variation (HRV).

An example of the overall process is illustrated in FIGS. 13A-13C. InFIG. 13A, the over-generated candidate peaks 104 are determined, each ofwhich is classified by the per-sensor classifier. In FIG. 13B,classification scores 106 for each of the peaks is shown. As can be seenin FIG. 13B, only a small number of the candidate peaks 104 have apositive peak score 106. FIG. 13C shows the fusion stages using theaggregated scores across sensors. In FIG. 13C, the smaller regions 108represent J-peaks that are detected first since the fused score ispositive, and the larger region 110 is a secondary search stage wherethe next highest score is selected to fill a missing peak. The regions108, 110 are used to determine estimated J-peak locations 112.

Performance Study

User studies were conducted to evaluate and validate the performance ofthe garment system 10.

Study Participants

The user study included 21 participants ranging in age from 22 to 38years. Nine (9) of the participants were female and twelve (12) weremale. The participants varied in weight from about 107 pounds (about48.5 kg) to about 240 pounds (about 109 kg), and in height from about 61inches, i.e., 5 feet and 1 inch (about 155 cm) to about 76 inches, i.e.,6 feet and 4 inches (about 193 cm).

The participants wore an example of the garment system 10 in variousstationary conditions and the output voltage was recorded. The examplegarment system 10 was made with a garment 12 that was a typicalextra-large (XL) sized pajama shirt. However, the participants were notrestricted solely to this size because many users select sleepwear thatis larger than their normal size. Also, even if a participant wouldnormally wear an XL sized shirt, due to variance in sizing amongstdifferent manufacturers garments rarely fit exactly to an individual'ssize.

Participants were separated into two (2) groups when analyzing the data.The first group included participants for whom the XL sized garment 12fits relatively well, and the second group included those participantswho are generally too short to wear the XL sized garment 12. For thesake of brevity, the first group of participants are referred tohereinafter as “height matched” participants and the second group orparticipants are referred to hereinafter as “height unmatched,”respectively. The height matched participants included eleven (11) ofthe participants who ranged in height from about 67 inches (about 170cm) to about 76 inches (about 193 cm). The height unmatched participantsincluded the remaining ten (10) participants with a height below 67inches (about 170 cm). The group of height unmatched participants variedquite a bit in body type and included both relatively short andrelatively thin individuals (in a couple of instances, the garment shirtreached just above the participant's knee). In short, the participantswere able to measure performance of the garment system 10 across variousdimensions.

Data Collection Methods

Data was collected in a variety of postures. In particular, because oneknown application for the garment system 10 is sleep sensing, the studyfocused on measurement when the participants were in sleep postures. Thesleeping postures that were studied are widely classified into sixcategories as shown in FIGS. 15A-15F. FIG. 15A shows a first sleepposture 114 where the participant is lying on his or her side with armsextend to the side and legs extended down, also referred to herein the“yearner posture 114.” FIG. 15B shows a second sleep posture 116 wherethe participant is lying in a prone or face down posture, also referredto herein as the “freefaller posture 116.” FIG. 15C shows a third sleepposture 118 where the participant is lying in a supine or face up withhis or her arms extended in the superior direction (e.g., toward thehead) at least partially, also referred to herein as the “starfishposture 118.” FIG. 15D shows a fourth sleep posture 120 where theparticipant is lying in a supine or face up posture with his or her armsextended in the inferior direction (e.g., toward the feet), alsoreferred to herein as the “soldier posture 120.” FIG. 15E shows a fifthsleep posture 122 where the participant is lying on his or her side withthe arms and legs partially tucked, also referred to herein as the“fetal posture 122.” FIG. 15F shows a sixth sleep posture 124 where theparticipant is lying on his or her side with the arms and legs bothextended straight or substantially straight in the inferior direction(e.g., toward the feet), also referred to as the “log posture 124.” Datawas collected from participants in each of the sleep postures 114-124shown in FIGS. 15A-15F. In addition to the sleep postures 114-124,performance was also evaluated with participants sitting on a chair(e.g., as shown in FIG. 1B) and standing (e.g., as shown in FIG. 1C) astwo additional postures of interest since they provide a contrastagainst sleep postures. In particular, standing represents a difficultscenario because there is minimal pressure against an external surfaceon which the resistive sensors 14 can rely. Collectively, the six (6)sleep postures 114-124, the sitting posture of FIG. 1B, and the standingposture of FIG. 1C are hereinafter referred to as the “tested postures”for the sake of brevity.

The duration of each measurement for each of the tested postures was forone (1) minute, for a total of eight (8) minutes of recording from eachindividual participant. Each recording included five channels, four (4)corresponding to each of the resistive sensors and one (1) correspondingto the triboelectric sensor. Since the garment system 10 is designed tomeasure vital signs, a ground truth for the physiological signals wasalso recorded. For heart rate, an ECG signal was used as a reference forheartbeats, and a photoplethysmography (PPG) sensor for trackingrespiration.

Resistive Sensor Benchmarks

Various aspects of the resistive sensor 14 were benchmarked, asdescribed below.

Pressure Sensitivity

An experiment was run to validate that the resistive sensor 14 issensitive to a typical range of pressures experienced by and caused byhuman interaction. In the experiment, the pressure applied to theresistive sensor (with dimensions of 1.5 inches×2.7 inches, or about4.05 square inches (in²)) was changed in a controlled manner and theresulting resistance of the resistive sensor 14 was measured. Themeasurement was repeated ten (10) times for each pressure point byre-applying the pressure in various rotational directions and placementsto account for probable folds, asymmetry in functionalization andpressure distribution. FIG. 16 shows a box plot of the changes inresistance for the resistive sensors 14 as a function of the pressureapplied to one side of the sensor 14.

As can be seen in FIG. 16, the fabric resistance varies monotonically asthe amount of pressure applied to the resistive sensor 14B is increased.The sensitivity of the resistive sensor 14 is inversely related to theamount of pressure applied on the fabric surface of the garment 12. As areference, FIG. 16 also includes lines 126 and 128, which corresponds tothe pressure applied onto the rear resistive sensor 14B by participantsweighing about 107 pounds (about 48.5 kg) and about 240 pounds (about108.9 kg), respectively, when the participants are lying supine (faceup). The data of FIG. 16 shows that the resistive sensors 14 areslightly more sensitive to lighter individuals and less sensitive toheavier individuals. However, overall the results show that theresistive sensors 14 have good sensitivity in the typical regime ofhuman weight.

Determining Posture Through Pressure Measurements

When used in conjunction, the distributed set of resistive sensors 14can provide very accurate information about posture. FIG. 17 is a graphshowing how the pressure baseline (e.g., the V_(Press) signal in FIG. 5)for each of the resistive sensors 14A, 14B, 14C, and 14D changes as thewearer of the garment system 12 transitions between different postures.Specifically, FIG. 17 shows the pressure baseline change as the wearertransitions, at about 30 seconds, from the soldier posture 120 of FIG.15D to the fetal posture 122 of FIG. 15E and then, at about 70 seconds,from the fetal posture 122 of FIG. 15E to the prone or freefallerposture 116 of FIG. 15B and vice versa. Note that the voltage beingmeasured via the example voltage divider circuit shown in FIG. 5 isinversely proportional to the pressure, so lower voltage means higherpressure. As can be seen by FIG. 17, the resistive sensors 14 that areunder pressure have reduced resistance and show lower voltage allowingthe resistive sensors 14 to reliably measure pressure.

FIG. 17 shows clear trends. In the supine soldier posture 120 (FIG.15D), which occurred from 0 seconds to about 30 seconds, the rearresistive sensor 14B has the lowest voltage and the front resistivesensor 14A has the highest voltage. The left resistive sensor 14C andthe right resistive sensor 14D that measure arm pressure are less clearindicators of the posture in the case of the supine soldier posture 120.When the subject transitions to the fetal posture 122 (FIG. 15E) atabout 30 seconds, the left resistive sensor 14C becomes pressuredbecause the participants were instructed to lie on their left side whenin the fetal posture 122, whereas the front resistive sensor 14A, therear resistive sensor 14B, and the right resistive sensor 14D are notunder much pressure. Finally, in the prone or freefaller posture 116(FIG. 15B), the front resistive sensor 14A sees the highest pressure (aswould be expected), while the rear resistive sensor 14B shows a muchlower pressure (as would be expected). Thus, posture-dependent changesin pressure detected by the resistive sensors 14 are clearly observable.In an example, a simple decision tree that looks for differences betweenthe front resistive sensor 14A and the rear resistive sensor 14B and/orbetween the front resistive sensor 14A and the left and right resistivesensors 14C and 14D and/or between the rear resistive sensor 14B and theleft and right resistive sensors 14C and 14D can easily identify posturewith near 100% accuracy across all wearers of the garment system 10. Itis even possible to differentiate between similar postures, such asbetween the two supine postures (i.e., the starfish posture 118 and thesoldier posture 120) due to differences in pressure for the left andright resistive sensors or between the side-lying postures (i.e., thefetal posture 122, the yearner posture 114, and the log posture 124) dueto differences in pressure for the front and rear resistive sensors 14Aand 14B and the left and right resistive sensors 14C and 14D, as will beappreciated by those of skill in the art.

Measuring Physiological Parameters

The performance of the garment system 10 in detecting and measuringphysiological variables of interes—including, but not necessarilylimited to, respiration rate, heart rate, and heart rate variability.For the validation study, “heart rate variability” or “HRV” refers toroot-mean square differences of successive R-R intervals (hereinafter“RMSSD”), which is a common measure of HRV. These physiologicalvariables are determined according to the signal processing blocksdescribed above with respect to the signal processing pipeline 60 ofFIG. 8.

FIGS. 18-20 show the performance of the garment system 10 in measuringheart rate (also referred to as “HR”), HRV, and respiration rate (alsoreferred to as “RR”). FIG. 18 shows the error in estimating HR by thegarment system 10 in each of the eight (8) tested postures (i.e.,sitting (FIG. 1B), standing (FIG. 1C), yearner posture 114 (FIG. 15A),freefaller posture 116 (FIG. 15B), starfish posture 118 (FIG. 15C),soldier posture 120 (FIG. 15D), fetal posture 122 (FIG. 15E), and logposture 124 (FIG. 15F)). FIG. 19 shows the error in estimating HRV ineach of the tested postures. FIG. 20 shows the error in estimating RR ineach of the tested postures. Each of FIGS. 18-20 also split the databetween the height matched group and the height unmatched group.

FIGS. 18-20 show results that are very good across all threephysiological parameters. When measuring heart rate, for theheight-matched participants error in HR estimation was generally lessthan 1 beat per minute (“bpm”) and error in HRV estimation was less than0.5%. The only posture that had relatively high error was the standingposture (FIG. 1C), which is to be expected because there is noexternally pressured surface beyond typical atmospheric pressure to acton the resistance sensors 14, so the garment system 10 must rely onweaker signals from the pressure of the arm against the torso for theleft and right resistive sensors 14C and 14D and the triboelectricsensor 16 resting on the wearer's stomach. But even in the standingcase, the error is not too high, e.g., the median HR error was about 2.5bpm and median HRV error was about 2%. For the height unmatchedparticipants, the upper quartile and worst-case error is more but themedian error is only a little more than then height-matched case(roughly 2 bpm for the HR error and 2% for the HRV error). In all cases,the results are better for the height-matched participants compared tothe height-unmatched participants.

The respiration metrics were also very good. The median error wasgenerally below 1 respiration per minute (“resp/min”). The error washigher for the starfish posture 118 and the soldier posture 120. Withoutwishing to be bound by any theory, the inventors believe this was sobecause the rear resistive sensor 14B saw a weaker respiration signaldue to the wearer's spine, and because the triboelectric sensor 16 onthe stomach did not help since it cannot measure slow baseline changes(which is the case with respiration). The signal in these postures 118and 120 are primarily from the front resistive sensor 14A on the chestand sensor fusion is less useful in these postures 118, 120 leading tohigher error.

Performance of Signal Processing Pipeline

Having discussed the application-level benchmark metrics, theperformance of the garment system 10 was also evaluated for each blockof the signal processing pipeline 60 of FIG. 8. First, the evaluationlooked at the overall results for J-peak detection via the entirepipeline 60. Then various stages in the signal processing pipeline 60were compared to see their effect in overall accuracy.

J-Peak Detection

The effectiveness of distinguishing J-peaks amongst all candidate peaksby the signal processing pipeline 60 was evaluated. F₁-score was used asa measure of performance of the classification 90, which was performedusing Leave-One-Subject-Out (LOSO). In this result, the height-matchedparticipants were not distinguished from the height-unmatchedparticipants, and instead the process aggregated results for allparticipants.

Table 1 below lists the F₁ scores from each of the resistive sensors 14in each of the tested postures (sitting, standing, and the sleeppostures 114-124) as determined from the classification stage 90 of thesignal processing pipeline 60 of FIG. 8, prior to fusion 96 of the datain the post-processing stage 100. As can be seen in Table 1, there issubstantial variation in the results and the scores are relatively poorfor some individual sensors 14. For example, the rear resistive sensor14B can have poor performance when there is too much pressure on it(such as in the starfish posture 118) or when there is little to nopressure at all (such as in the fetal posture 122). However, the rearresistive sensor 14B offers very good performance in some otherpositions (e.g., the freefaller posture 116 and when standing).Similarly, each resistive sensor 14 performs better in some scenariosand worse in others. Also, it is important to note that no singleresistive sensor 14 received an F₁ score above 90%. Rather, in mostcases, it hovers between about 75% and about 80%.

TABLE 1 Median F₁ Score Before Fusion Resistive Sensor Front Rear LeftRight Posture (14A) (14B) (14C) (14D) Fetal 66.1 43.0 71.8 68.4Freefaller 776.6 80.9 79.4 75.7 Log 70.1 44.8 67.2 76.7 Sitting 71.066.2 75.3 65.7 Soldier 61.9 62.6 82.2 88.4 e 68.4 78.0 81.4 78.2Starfish 84.2 40.0 73.4 79.7 Yearner 69.9 64.5 74.8 73.8

FIG. 21 is a bar graph showing the F₁ score for each of the testedpostures after fusion 96 of the data in the post-processing stage 100 ofthe signal processing pipeline 60. As can be seen in FIG. 21, the F₁scores after fusion 96 are considerably higher, and the median F₁ scorewas often close to 100% and was above 95% for almost all the testedpostures. The highest error was for the standing posture, and it isbelieved that this can be accounted for due to reasons discussed above.The upper quartiles have somewhat higher error—this is primarily becauseof the height-unmatched participants whose error was higher than theheight-matched participants.

Error Contribution Breakdown

A breakdown of the error contributions of various aspects of the garmentsystem 10 and the signal processing pipeline 60 is discussed below.

Signal Processing Pipeline Stage Contribution

For this breakdown measure, results were analyzed by selectivelychoosing blocks of the signal processing pipeline 60 and measuring theaccuracy of the garment system 10. In this breakdown, three differentsignal processing algorithms were considered. The first algorithmcorresponds to the best-case performance when a single sensor was used,which is defined as the best sensor for each participant and posture forthese numbers, i.e., the error that results after the classificationstep 90 but before the sensor fusion 96 in the pipeline 60 for eachsensor. This is not viable in practice, but it provides an upper boundon single-sensor performance. The second algorithm fuses the posteriorprobabilities across the sensors, i.e., the error that results after thesensor fusion 96 of the pipeline 60, without weighting them by thequality index. The third algorithm is essentially the full signalprocessing pipeline 60, i.e., with weighting of the data based on thesignal quality index 98, as discussed above.

FIGS. 22 and 23 show the error results for each of these algorithms:i.e., (1) the error after the classification 90 but before the sensorfusion 96 (labeled as “(1): CLASSIFICATION” in FIGS. 22 and 23); (2) theerror after the probabilities have been fused 96 across all the sensorswithout weighting with the signal quality index 98 (labeled as “(2):(1)+SENSOR FUSION” in FIGS. 22 and 23); and (3) the error afterweighting the data using the signal quality index 98 (labeled as “(3):(2)+SQI” in FIGS. 22 and 23).

FIG. 22 shows the error contribution to the average heart ratevariability (HRV) and FIG. 23 shows the error contribution to theaverage heart rate. FIGS. 22 and 23 demonstrate that the sensor fusion96 greatly reduces the system error, with about a six times (6×)reduction for the height-matched participants and about a two and a halftimes (2.5×) reduction for the height-unmatched participants. The use ofa weighted measure using the SQI 98 improves results further, e.g.,about two times (2×) for the height-matched participants and about 25%for the height-unmatched participants. As is further shown in FIGS. 22and 23, the sensor fusion 96 and the SQI weighting 98 in the signalprocessing pipeline 60 are generally more effective for theheight-matched participants than for the height-unmatched participants.This is intuitive since the garment system 10 provides signals frommultiple sensors 14, 16 for the height-matched participants such thatthe sensor fusion 96 works better. However, the results are not muchworse for the height-unmatched participants, such that even if thewearer chooses to have an oversized garment shirt 12, the garment system10 should be able to measure physiological parameters with reasonableaccuracy.

These results show the benefits of having a distributed array of sensors14, 16 on the garment system 10. Unlike earlier wearables likesmartwatches that can measure essentially only at a single point on thebody, the example garment system 10 has multiple distributed sensors 14,16, e.g., two sensors in examples where only a single resistive sensor14 and a single triboelectric sensor 16 is used, up to five or moredistributed sensors (i.e., four or more resistive sensors 14 and one ormore triboelectric sensors 16 at various positions of the garment 12)whose information is fused such that the garment system 10 describedherein can capture a strong signal even if one or two sensors 14, 16 areerroneous due to their positioning.

Individual Resitive Sensor Contribution

Next, the contribution of each resistive sensor 14 to the overallresults of the garment system 10 and whether there are specific sensors14 that are superior to others in terms of determining physiologicalparameters was investigated. FIGS. 24 and 25 are plots of the accuracyof the garment system 10 if only one resistive sensor 14 (i.e., onlyfront sensor 14A, only rear sensor 14B, only left sensor 14C, and onlyright sensor 14D) was used and contrast this with the overall accuracywhen the information from all of the sensors 14A, 14B, 14C, and 14D arefused together. FIG. 24 shows the error values for average heart rateerror, and FIG. 25 shows the error values for the average HRV error.

As can be seen in FIGS. 24 and 25, each resistive sensor 14A, 14B, 14C,and 14D has high error in its own estimate of heart rate and HRV.However, after the sensor fusion 96, the estimation error drops by about400% to about 500%. This result also highlights the benefits of thesensor fusion 96 and shows that any one resistive sensor 14 is notexpected to do as well as fusing readings from multiple sensors 14.

Triboelectric Sensor Contribution

The contribution of the triboelectric sensor 16 to the overallclassification performance was also investigated by comparing theaccuracy of the garment system 10 after data fusion 96 of only theresistive sensors 14 are used (i.e., without data from the triboelectricsensor 16) with that of the garment system 10 after data fusion 96 ofall of the sensors 14, 16 are used (i.e., with data from all of theresistive sensors 14 and the triboelectric sensor 16). FIG. 26 is bargraph of the F₁ scores for all of the postures (i.e., standing, sitting(FIG. 1B), standing (FIG. 1C), and yearner 114, freefaller 116, starfish118, soldier 120, fetal 122, and log 124 from FIGS. 15A-15F) both withand without data from the triboelectric sensor 16. As can be seen byFIG. 26, inclusion of the envelope features from the triboelectricsensor 16 is informative and improves overall performance by improvingthe median F₁ score and reducing the number of outlier data points.

Subjective Factors

Each participant was asked to complete a survey regarding severalsubjective aspects of the example garment system 10. First, eachparticipant was asked to rate the comfort of the example garment systemon a subjective scale of 1-5 (with 1 being very uncomfortable and 5being very comfortable). The average subjective comfort rating amongstthe 21 participants was 4.95.

Next, each participant was asked if they would be generally interestedin tracking vital signs (e.g., heart rate, respiration rate, and HRV)during sleep to determine the general inclination toward logging vitalsigns during sleep. Seventeen (17) of the participants, or about 81%,reported that they would be interested in tracking vital signs duringsleep. Four (4) participants, or about 19%, said that they generallywould not be interested.

Third, each participant was asked if they would prefer using the examplegarment system 10 or a wrist-worn fitness band (e.g., a FITBIT) to trackvital signs during sleep. Sixteen (16) participants, or about 76%,reported that they would prefer the example garment system 10 to awrist-worn fitness tracker for tracking vital signs during sleep. Five(5) participants, or about 24%, said they would prefer the wrist-wornfitness tracker.

Finally, each participant was asked if the example garment system 10interrupted respiration or impacted their respiration pattern. Alltwenty-one (21) participants reported that the example garment system 10did not interfere with breathing at all.

The data of the participants' subjective impressions show that at leastthis group of participants found the example garment system 10 to becomfortable and unobtrusive. A sizable percentage of the participantsalso indicated they would prefer the comfort of the example garmentsystem 10 to a wrist-worn fitness tracker like a FITBIT. The inventorsbelieve that a major advantage of the example garment system 10described herein is the comfortable and unobtrusive nature of itsdesign. The sensors 14, 16 can be integrated into everyday nightwearwith discrete elements placed in expected locations like a button. Inaddition, users do not need to remember to wear an additional devicethat would be unusual during sleep, like a fitness band.

Although the present disclosure describes the garment system 10 as beingused for measurement of vital signs, such as for sleep tracking andevaluation, those of skill in the art will appreciate that the garmentsystem 10 and methods described herein are not limited to sleep. Rather,the example garment system 10 and methods described herein may be usablefor fitness applications, such as heart rate and respiration monitoringduring exercise. Moreover, the example garment systems and methodsdescribed herein are not limited to health applications like sleep andfitness tracking. The example garment system 10 and methods describedherein may be used in other contexts, such as to provide sensors forintegration into a virtual reality (VR) system, such as to assist in thegeneration of VR haptics.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments in which theinvention can be practiced. These embodiments are also referred toherein as “examples.” Such examples can include elements in addition tothose shown or described. However, the present inventors alsocontemplate examples in which only those elements shown or described areprovided. Moreover, the present inventors also contemplate examplesusing any combination or permutation of those elements shown ordescribed (or one or more aspects thereof), either with respect to aparticular example (or one or more aspects thereof), or with respect toother examples (or one or more aspects thereof) shown or describedherein.

In the event of inconsistent usages between this document and anydocuments so incorporated by reference, the usage in this documentcontrols.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments can be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is provided to allowthe reader to quickly ascertain the nature of the technical disclosure.It is submitted with the understanding that it will not be used tointerpret or limit the scope or meaning of the claims. Also, in theabove Detailed Description, various features may be grouped together tostreamline the disclosure. This should not be interpreted as intendingthat an unclaimed disclosed feature is essential to any claim. Rather,inventive subject matter may lie in less than all features of aparticular disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description as examples or embodiments,with each claim standing on its own as a separate embodiment, and it iscontemplated that such embodiments can be combined with each other invarious combinations or permutations. The scope of the invention shouldbe determined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

1. A textile-based garment system comprising: a garment substrate formedfrom one or more textile-based sheets; a distributed array of aplurality of resistive pressure sensors coupled to the garment substrateat a set of first specified locations, wherein each of the plurality ofresistive pressure sensors comprises; a pair of first textile-basedouter layers each having an electrical resistance of no more than 100ohms, and a textile-based inner layer sandwiched between the pair offirst textile-based outer layers having an electrical resistance of atleast 1 mega-ohm; and electronics configured to process signals from thedistributed array of resistive pressure sensors to determine one or morephysiological properties of a wearer of the garment substrate.
 2. Thetextile-based garment system of claim 1, wherein the textile-based innerlayer comprises a first textile substrate with one or morefunctionalized coating materials deposited thereon.
 3. The textile-basedgarment system of claim 2, wherein the one or more functionalizedcoating materials comprise an ion-conductive material.
 4. Atextile-based garment system of claim 1, further comprising one or moretriboelectric sensors coupled to the garment at one or more secondspecified locations, wherein each of the one or more triboelectricsensors comprises; a pair of second textile-based outer layers eachhaving an electrical resistance of no more than 100 ohms, and atextile-based triboelectric core sandwiched between the pair of secondtextile-based outer layers, wherein the textile-based triboelectric corecomprises a first textile-based dielectric layer and a secondtextile-based dielectric layer, wherein the first textile-baseddielectric layer comprises a first textile-based dielectric materialthat forms a positively-charged triboelectric surface, and wherein thesecond textile-based dielectric layer comprises a second textile-baseddielectric material that forms a negatively-charged triboelectricsurface, wherein the positively-charged triboelectric surface of thefirst textile-based dielectric layer is adjacent and proximate to thenegatively-charged triboelectric surface of the second textile-baseddielectric layer, and wherein the electronics are configured to processsignals from the one or more resistive pressure sensors and the one ormore triboelectric sensors in addition to the distributed array ofresistive sensors to determine one or more physiological properties ofthe wearer of the garment substrate.
 5. The textile-based garment systemof claim 4, wherein one or both of the second textile-based outer layersof the triboelectric sensor comprise a silver nylon textile.
 6. Thetextile-based garment system of claim 4, wherein the first textile-baseddielectric material comprises a second textile substrate that isfunctionalized with one or more silane moieties comprising one or moreamine groups to provide the positively-charged triboelectric surface. 7.The textile-based garment system of claim 6, wherein the second textilesubstrate is functionalized with aminopropyl siloxane.
 8. (canceled) 9.The textile-based garment system of claim 4, wherein the secondtextile-based dielectric material comprises a third textile substratecoated with a negatively-charged triboelectric material to provide thenegatively-charged triboelectric surface.
 10. The textile-based garmentsystem of claim 9, wherein the negatively-charged triboelectric materialcomprises a polyurethane.
 11. The textile-based garment system of claim9, wherein the third textile substrate comprises a ripstop nylon. 12.The textile-based garment system of claim 1, wherein the one or morephysiological properties of the wearer includes one or more of: one ormore cardiac properties of the wearer, one or more respiratoryproperties of the wearer, and one or more posture properties of thewearer.
 13. A resistive pressure sensor comprising: a pair oftextile-based outer layers each having an electrical resistance of nomore than 100 ohms; and a textile-based inner layer sandwiched betweenthe pair of textile-based outer layers having an electrical resistanceof at least 1 mega-ohm.
 14. The resistive pressure sensor of claim 13,wherein one or both of the textile-based outer layers comprise a silvernylon textile substrate.
 15. The resistive pressure sensor of claim 13,wherein the textile-based inner layer comprises a first textilesubstrate with one or more functionalized coating materials depositedthereon.
 16. The resistive pressure sensor of claim 15, wherein the oneor more functionalized coating materials comprise an ion-conductivematerial.
 17. The resistive pressure sensor of claim 16, wherein the ionion-conductive material comprises at least one of: a siloxane containingone or more quaternary ammonium moieties, or a p-dopedpoly(3,4-ethylenedioxythiophene).
 18. The resistive pressure sensor ofclaim 17, wherein the p-doped poly(3,4-ethylenedioxythiophene has theformula:

where n is the number of repeat units.
 19. The resistive pressure sensorof claim 17, wherein the ion-conductive material comprisesN-trimethoxysilylpropyl-N,N,N,-trimethylammonium chloride.
 20. Theresistive pressure sensor of claim 15, wherein the first textilesubstrate comprises cotton.
 21. The resistive pressure sensor of claim13, wherein the resistive pressure sensor produces signals correspondingto a pressure applied to the resistive pressure sensor.